Training course · Generative AI · Compliance & Data Protection

Generative AI for Compliance and Data Protection Teams

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Generative AI for Compliance and Data Protection Teams” is designed for roles such as Compliance officers and managers, Data protection officers and privacy coordinators and Information security officers and shows step by step how to use Generative AI productively, safely and transparently in Compliance & Data Protection – from the fundamentals to advanced workflows.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

Compliance and data protection teams must keep track of a growing body of rules, from the GDPR and whistleblower protection to supply chain due diligence and the EU AI Act. Generative AI can structure and accelerate this work: AI assistants summarise new regulations, produce gap analyses against existing policies, support records of processing activities and data protection impact assessments and draft clear training content. Automation helps with data subject requests, contract reviews and vendor risk assessments. At the same time, compliance teams are expected to steer AI use across the organisation: AI policies, risk classification under the AI Act and staff AI literacy obligations. Teams that use AI confidently themselves can advise the business better and design effective governance.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Compliance officers and managers
  • Data protection officers and privacy coordinators
  • Information security officers
  • Governance, risk and compliance staff
  • AI governance leads

Typical challenges we address

  • Tracking numerous new regulations and translating them into internal requirements
  • Keeping records of processing and impact assessments up to date
  • Advising business units quickly and soundly when they introduce new tools
  • Steering organisation-wide AI use in a compliant way

What you will be able to do

  • You can explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • Use AI for regulatory analysis, data protection documentation and contract reviews.
  • Build AI governance with a policy, a tool inventory and risk classification.
  • Advise business units soundly on GDPR and AI Act requirements for AI use.

Course outline

Module 1 How large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
Module 6 Practice lab: Compliance & Data Protection
  • Regulatory gap analysis
  • Records of processing and DPIAs
  • Reviewing data processing agreements
  • Data subject requests
  • Building AI governance
  • Training and awareness
Module 7 Data protection, quality & transfer to daily work
  • Compliance and privacy records often contain sensitive information, such as whistleblower reports that must be kept strictly confidential under whistleblower protection laws and must not be entered into unvetted AI tools. At the same time, Article 4 of the EU AI Act requires organisations to ensure sufficient AI literacy among their staff.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Regulatory gap analysis

An AI assistant summarises new regulations, compares requirements with existing policies and processes and produces a structured list of gaps and actions for expert assessment.

Records of processing and DPIAs

Process descriptions and interviews are turned into draft entries for the record of processing activities and risk analyses for data protection impact assessments, reviewed by the DPO.

Reviewing data processing agreements

AI checks vendors' data processing agreements and privacy documentation for the mandatory content under Article 28 GDPR, third-country transfers and technical measures and flags deviations from the internal standard.

Data subject requests

Automations log access and erasure requests, monitor deadlines and draft responses, while data searches and final approval remain with the responsible staff.

Building AI governance

AI helps draft AI policies, build an inventory of AI systems in use and classify them according to the EU AI Act risk categories as a basis for governance board decisions.

Training and awareness

Policies are turned into clear learning modules, case studies and quizzes on data protection, anti-money laundering, anti-corruption or AI literacy, tailored to target groups across the organisation.

Prerequisites

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. You work in compliance, data protection or information security and are familiar with the basics of the GDPR.

Technical requirements

You will need a laptop with an up-to-date browser and internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

Data protection & compliance

Compliance and privacy records often contain sensitive information, such as whistleblower reports that must be kept strictly confidential under whistleblower protection laws and must not be entered into unvetted AI tools. At the same time, Article 4 of the EU AI Act requires organisations to ensure sufficient AI literacy among their staff.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 Online Request
  • Thu, 10 December 2026 Salzburg Request
  • Wed, 16 December 2026 Online Request
  • Wed, 13 January 2027 Online Request
  • Wed, 27 January 2027 Bremen Request
  • Mon, 1 February 2027 Online Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

What does the EU AI Act actually require of companies?

Since early 2025, certain AI practices have been prohibited and organisations must ensure AI literacy among staff. Further obligations cover transparency and providers and deployers of high-risk systems. In the course we classify typical use cases and work out which governance components your organisation needs.

Can data protection officers use AI themselves?

Yes, for example for contract reviews, draft documentation or training content, provided no unnecessary personal data is processed. The legal assessment remains with you. Hands-on experience with AI also improves your advice to the business, because you can judge opportunities and risks more realistically.

How much does “Generative AI for Compliance and Data Protection Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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